Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program

Chen Zhu () and Kazuyuki Motohashi
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Chen Zhu: Department of Technology Management for Innovation, The University of Tokyo

Scientometrics, 2023, vol. 128, issue 5, No 21, 3035-3065

Abstract: Abstract This study investigates the impact of government R&D spending on promoting technology convergence. We test the hypotheses that a government funding program positively affects technology convergence, and that the effects vary depending on the participant (i.e., academic and industrial inventors). We used the Advanced Sequencing Technology Program (ASTP) as an example to investigate this issue. We develop a novel dataset by linking the ASTP grantee information with the PATSTAT patent database. On this basis, we develop inventor-level characteristics for propensity score matching, selecting a control group of inventors from among those enrolled in the ASTP. Then, we employ difference-in-difference models to assess the program’s impact on the matched sample. The results support the program’s role as a driving force of technology convergence. The findings also indicate that the program has a greater influence on industry inventors than on academic counterparts. Furthermore, we conceptualize the program’s “leverage effect” and demonstrate that it can attract more external industrial inventors than academic inventors. The work advances our understanding of the role of a government-funded program in encouraging convergence and has implications for developing convergence-related R&D programs in the future.

Keywords: Technology convergence; NIH program; Policy analysis (search for similar items in EconPapers)
JEL-codes: O33 O38 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-023-04682-w

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